Ross ROSS = Recommend OSS · open-source software intelligence for agents

DEEIX-AI/DEEIX-Chat

An enterprise AI workspace for model routing, multimodal chat, files, tools, billing, identity, and operations. observed · 2026-08-28

github.com/DEEIX-AI/DEEIX-Chat · homepage · Go · Apache-2.0 (permissive) observed · 2026-08-28

Health v2 · maintenance only

80/100

  • Activity 99
  • Release rhythm 98
  • Longevity 7

Flags: young

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: 3.5
  • age_days: 104
  • days_rel: 13
  • days_push: 7
  • n_releases_24m: 19

Full methodology

Adoption not part of the score

1352 stars · 200 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

DEEIX Chat is an open-source, self-hostable enterprise AI workspace that unifies access to multiple LLM providers behind one entry point, with multimodal chat, model routing, files and RAG, MCP tools, billing, identity, and operational controls. It is built with a Next.js frontend and a Go backend and is designed for lightweight, reliable deployment for individuals, teams, and enterprises.

Use cases

  • self-host a multi-user chatgpt-style workspace for my team
  • route requests across OpenAI, Anthropic, Gemini and other providers from one endpoint
  • chat with my uploaded documents using RAG and embeddings
  • connect MCP tools to an internal AI assistant
  • manage model access, quotas, and usage billing for an organization
  • add SSO and audit logging to an internal LLM deployment
  • process and OCR uploaded files into conversation context

When to choose

  • you need one self-hosted gateway and UI for multiple LLM providers
  • you want built-in RAG, file processing, and MCP tool support without assembling separate services
  • you need enterprise features like billing, identity, 2FA/SSO, and audit logs
  • you want a lightweight Go/Next.js stack with low runtime footprint

When to avoid

  • you only need a simple single-provider chat UI with no admin or routing needs
  • you want a mature product with a long track record and large community
  • you need deep customization of the frontend beyond what the product exposes
  • you require provider features not covered by its OpenAI/Anthropic/Gemini/xAI/OpenRouter adapters

Facets

application · maturity active

chatbot rag mcp llm-inference api-gateway search-engine ocr file-upload auth large-language-models chatbots self-hosted web-development self-hosted go model-routing multi-provider chat-ui enterprise-ai nextjs pgvector usage-billing sso billing retrieval-augmented-generation ai-agents web-server docker nodejs

3 sources

Member repositories

RepositoryRoleHealth v2
DEEIX-AI/DEEIX-Chatmain80

For agents

markdown · JSON · MCP: product_card(name="DEEIX-AI/DEEIX-Chat")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem